Our other neural baseline is the biencoder model (BEM) for WSD introduced by [1]}. The BEM has two independent encoders, a context encoder that processes the context sentence (including the target word) and a gloss encoder that encodes the glosses of senses into a sense representation. The BEM takes the dot product of the target word representation from the context encoder and sense representations from the gloss encoder, and it labels the target word with the sense that has the highest dot product score. We train BEM\(_{BERT}\) by initializing each encoder with BERT and training on the FEWS train set.
